Where does enterprise search end and text analytics begin?

I was asked recently by a Chief Information Officer (CIO) of a large organization, where does search

end and text analytics begin? It is an interesting question perhaps worth exploring and developing

some models, one of which is shown below (Figure 1).

Figure 1 – From ‘traditional search’ (green) to unlocking a wider range of questions we can ask

The green boxes (Figure 1) are the domain of traditional search and the blue boxes illustrate the

direction of travel being taken by some organizations blending increasing amounts of text analytics

and Knowledge Organization Systems (KOS) like taxonomies and thesauri to traditional Information

Retrieval (IR). From left to right, this allows organizations to move beyond just finding documents

which contain existing ‘knowledge’, to pattern recognition (where the searcher is part of the process)

discovering latent ‘knowledge’ directly, sometimes using the whole corpus of text available.

Whether you call this space Knowledge Management, e-business, Artificial Intelligence (AI), smart

machines, deep learning, cognitive computing or plain old search & discovery probably does not

matter.

Ranking by frequency dominates

Text has been converted into numerical representations of some form since the first search engine

was developed. Statistical approaches for search have generally been dominated by frequency. Whilst

different metadata fields are typically tuned with different search ranking criteria (e.g. title and tags

higher weights compared to body text), all things being equal its statistical frequency that dominates

ranking. Whether that is how many times search terms have been mentioned in a document/corpus

or how many times people have made certain search queries (for query suggestions as you type) or

how many times a web page or document has been referenced or accessed. Even facets (refiners)

typically shown on the left hand side of a search user interface to help narrow a search, are almost

always ranked by frequency or popularity, regardless of whether they are created from manual or

automatic tagging methods. Ranking by frequency dominates.

I won’t delve into ‘relevance’ in this article, needless to say ranking by statistical frequency has been

used as a part-surrogate for relevance.

Linguistics

Linguistics (including the use of authority lists, thesauri/taxonomies, ontologies for handling synonyms

& relationships and Natural Language Programming (NLP)) have been used for decades to improve

searching. These techniques are typically used to ensure the above methods operate on ‘concepts’ to

mitigate the ‘vocabulary problem’ we have as humans and improve search recall and precision.

Thesauri, taxonomies and ontologies can also help recommend search terms. They are an aid to the

statistics, with many scholars proposing a ‘best of both worlds’ that hybrid (linguistic & statistical)

techniques work best to cater for a range of scenarios’ in search and auto-classification, although

some still argue for just one or the other.

Social cues

Social analytics has been used in search for many decades. For example, recommending or boosting

an item in search results because it is viewed or cited very often, suggesting search terms as you type

“the Google type model” or suggesting another information item that may be of interest, the

“Amazon.com type model” (often called crowdsourcing). These approaches are transactional, based

on the social cues from people. Enterprise social tools increasingly hosted in the cloud, perform the

same type of analysis, using the data from document or post views and likes, ‘people who attend online

meetings that you attended’ etc., to algorithmically push information to the user in an activity feed

like the “Facebook type model”, complementing traditional enterprise search. Aspects of

personalization in browser cookies have been doing this for years of course, by clickthrough

advertising revenue.

It could be argued that search has never been separate to analytics.

Content Analytics

As information volumes have grown exponentially, so has the analysis of the content inside these

‘containers’ of information, such as web pages, documents and structured databases. Linguistics is

still a very important part, but it supports the statistical methods that are used to seek out ‘interesting’

situational context.

Taxonomies and semantic networks are useful, if not essential for computer systems to help us

discover information, however, they may also blind us to new discoveries if we ignore what the data

(text) is telling us and only superimpose a priori representations.

Organizations have turned to auto-classification to help records retention, to reduce file storage costs

and clean up Redundant, Obsolete and Temporary (ROT) files typically on the shared file system and

email systems. Search is also used for reporting, highlighting information in the corpus that should not

be there for legal, privacy or confidentiality reasons. This analysis may often be simply a series of

phrase queries. Similar techniques have been (and continue to be used) automatically moving emails

into spam folders when they contained certain trigger words or with disambiguated semantics.

Perhaps these approaches are akin to First generation content analytics.

Second generation analytics could be viewed as more advanced techniques which target business

value, wealth creation and risk reduction to surface real world patterns. In addition to frequency, both

similarity and discriminatory techniques are increasingly used. In these techniques conversion of text

to numerical form is taken to extremes. For example the creation of complex probability distributions

using neural networks approximating one words relationship to every word in the entire corpus where

vectors can be compared, added and subtracted.

Instead of using this analytical information to influence search results of document and web pages,

the focus shifts to the associations between concepts and entities within and across those documents

and web pages. From documents to entities & concepts. It is still ‘search’ but a different focus.

Using an analogy, if documents are “atoms”, content analytics smashes them apart to look at the

concepts & entities “particles” inside and their behaviour with respect to one another. You might find

a new particle or a new relationship between particles that you did not know before, but you have to

look inside first and it takes imagination and energy to produce the really exciting.

Analogues

Organizations are increasingly interested in how these content analytic techniques can be used to

identify complex business analogues. As one scientist made the comment, “analogues are difficult to

search on because you don’t know what they are, so you don’t know what search queries to use!” For

example in the oil and gas industry looking for geological environments (using the similarity of words

that appear around different entities), and/or surface an activity trend that one company is doing in a

geological basin, that other companies are not. These techniques can transform unstructured

information into structured information to automatically return answers (not lists of documents), to

stimulate ideas, visualize results on a map or store in a database.

Prediction

Coping with information overload and keeping on top of what is going on around you is getting

increasing difficult in many areas. Using historical information to calibrate systems, may help predict

certain types of events before they happen. For example, looking at patterns from daily operations

reports as clues, alerting engineers to potential impending issues. They present another voice based

on the text, akin to “Look, last time I saw these clues appearing in the operations reports..this

happened”. These techniques have been used with quantitative data for many years to predict and

prescribe action, but are now being increasingly applied to qualitative (unstructured text) authored

by people.

Relevance versus Interestingness

In some recent research on facilitating serendipity in the search user interface, a scientist in an oil and

gas company mentioned a search result refiner was ‘relevant but not interesting’. Clearly what is

interesting for one person, may not be interesting for another. However, there may be a need to move

beyond traditional definitions (and algorithms) for relevance (Figure 2).

Figure 2 – Moving search beyond a text box and ten blue links

Summary

Returning to the question posed at the beginning, ‘Where does search end and text analytics begin?’

Perhaps they are two sides of the same coin. Text analytics has always been essential to basic

information retrieval, although its main use was to help people find results (containers) they were

looking for. Analytics of social cues have been used to good effect to help people locate what they

were looking for and also discover information that they were not looking for. However, some argue

this is discovery through the ‘rear view mirror’ and creates a filter bubble that does not encourage us

to stray from the beaten path. Combining search and content based text analytics presents us with

opportunities to help us formulate our needs, test hypotheses, predict events, unlock new knowledge

and increase the propensity of our user interfaces to stimulate fortuitous information discovery.

There are likely challenges for the CIO. How to meet existing complaints and needs (to find that single

web page or document) for all staff, whilst delivering the capability for advanced discovery to small

communities that may wish to mine external and internal information (much more than a simple e-

discovery solution) – which may lead to leaps in business value that cannot be predicted in advance.

Many organizations have already done this organically. It may be a mistake to think this can be

achieved with a single technology or user interface.

That brings further challenges with regards to costs, multiple indexing streams and complexity.

Business cases and creative architectures may exist however to meet both requirements. However,

this will require leadership, to set out a clear vision with careful planning and architecting. These

decisions need to be made against a backdrop of technology vendor propaganda and useful

information which are intertwined, making it sometimes difficult for objective and realistic views of

what is possible and what are the caveats.

Exponentially growing information volumes combined with proven techniques published in the public

domain, present an opportunity to expand our horizons; ‘To move the goalposts’, with respect to the

questions we can ask computer systems and what those computer systems can suggest to us; after

all, we may be asking the wrong questions.

More at: www.paulhcleverley.com

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